Mobile robots are increasingly playing a pivotal role in various fields, including environmental monitoring and search-and-rescue tasks. The ultimate development goal for robots is to achieve embodied artificial intelligence (embodied AI), which enables continuous learning and evolution through interactions with the environment. Motion planning and control are fundamental for robots to interact with environments and accomplish complex tasks effectively. With the rapid advancement of AI technologies, machine learning (ML) algorithms have been successfully applied to various robotic tasks. This survey provides a comprehensive overview and classification of learning-based motion planning and control approaches, which can help mobile robots achieve embodied AI. First, the existing approaches are categorized into five system architectures based on the modules applied by the ML algorithms. Second, a detailed review is provided on how reinforcement learning (RL), imitation learning, and the integration of ML with model predictive control (ML-MPC) techniques are applied across the different architectures. Additionally, the current state of research on several critical issues in embodied AI is presented, including safe learning control, Sim2Real transfer, and large language models (LLMs). Finally, the survey highlights some challenges in realizing embodied AI in real-world scenarios and suggests potential directions for future research. Unlike existing reviews that focus on specific tasks or categorize related work by ML algorithms, this survey constructs a taxonomy of existing research from a system architecture perspective. It emphasizes key technologies that facilitate real-world applications. We hope that our work will contribute to the advancement of embodied AI in the field of mobile robots.
Wang et al. (2026) studied this question.